Deep Learning for Multi-Sensor Earth Observation

1st Edition - February 1, 2025
Imprint: Elsevier
Editor: Sudipan Saha
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 6 4 8 4 - 9
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 6 4 8 5 - 6

Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insigh… Read more

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Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.

Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Acknowledgments
Part I: Introduction to multi-sensor data and artificial intelligence
Chapter 1: Deep learning for multi-sensor Earth observation: introductory notes
1.1. Introduction
1.2. Different sensors and modalities
1.3. Benefits of fusion
1.4. Traditional multi-sensor fusion
1.5. Emergence of deep learning
1.6. Motivation and contents of the book
Chapter 2: A basic introduction to deep learning
2.1. Introduction
2.2. Different learning paradigms
2.3. CNN
2.4. Image classification
2.4.1. LeNet-5
2.4.2. AlexNet
2.4.3. VGGNet
2.4.4. InceptionNet
2.4.5. ResNet
2.4.6. DenseNet
2.4.7. MobileNet
2.5. Semantic segmentation
2.6. Target detection
2.7. From attention to transformers
2.8. Autoencoder and generative models
2.9. Practices and tricks
2.9.1. Appropriate learning task
2.9.2. Data quality
2.9.3. Choice of appropriate model
2.9.4. Hyperparameter tuning
2.9.5. Regularization
2.9.6. Loss functions
2.10. Conclusion
Part II: Artificial intelligence for sensor-specific data analysis and fusion
Chapter 3: Deep learning processing of remotely sensed multi-spectral images
3.1. Introduction
3.1.1. Remotely sensed multi-spectral images
3.1.2. Deep learning methods taxonomy
3.1.3. Chapter overview
3.2. Image preprocessing techniques
3.2.1. Super-resolution
3.2.2. Image fusion
3.2.3. Segmentation
3.2.4. Image registration and enhancement
3.3. Image analysis
3.3.1. Change detection
3.3.2. Object detection and recognition
3.3.3. Scene classification
3.4. Conclusions and perspectives
Chapter 4: Deep learning and hyperspectral images
4.1. Introduction
4.2. Image classification
4.2.1. Limited label learning for HSI classification
4.3. Dimensionality reduction
4.3.1. Band selection
4.3.2. Feature extraction
4.4. Unmixing
4.5. Image enhancement
4.5.1. Denoising
4.5.2. Super-resolution
4.5.3. Inpainting
4.6. Change detection
4.7. Future direction
4.8. Summary
Chapter 5: Synthetic aperture radar image analysis in era of deep learning
5.1. Introduction
5.2. SAR despeckling
5.3. SAR image classification
5.4. SAR semantic segmentation
5.5. SAR target detection
5.6. SAR domain adaptation
5.7. SAR-optical transcoding
5.8. PolSAR image analysis
5.9. InSAR data analysis
5.10. SAR analysis benefits from other sensors
5.11. Conclusion
Chapter 6: Deep learning with lidar for Earth observation
6.1. Introduction
6.2. Deep learning for 3D object detection with lidar point cloud data
6.2.1. 3D object detection models
6.2.2. Semi-automatic annotations for individual trees
6.2.3. 3D detection of individual trees
6.3. Deep learning for 3D semantic segmentation of lidar point cloud data
6.3.1. 3D semantic segmentation models
6.3.2. Point MLP and point convolution
6.3.3. 3D semantic segmentation of urban structures
6.4. Benchmark point cloud datasets
6.4.1. Real versus synthetic benchmark datasets
6.4.2. Annotation strategy for benchmark datasets
6.4.3. Benchmark point cloud datasets for Earth observation
6.5. Conclusion
Chapter 7: Several sensors and modalities
7.1. Introduction
7.2. Multi-sensor differences
7.3. Geographic differences
7.4. Domain adaptation and generalization
7.5. Fusion
7.6. Multi-modal analysis
7.7. Architectures
7.8. Conclusion
Part III: Advanced concepts and architectures
Chapter 8: Self-supervised learning for multi-modal Earth observation data
8.1. Motivation
8.2. Self-supervised learning principle
8.2.1. Pre-training by predicting geometric patch relations
8.2.2. Contrastive learning
8.2.3. Masked autoencoding
8.3. Adaptation to remote sensing
8.3.1. Contrastive learning for Earth observation data
8.3.2. Masked autoencoders for Earth observation data
8.3.3. Foundation models and evaluation datasets
8.3.4. Conclusion
Chapter 9: Vision transformers and multi-sensor Earth observation
9.1. Introduction
9.2. Basics of transformers
9.3. Vision transformer
9.4. Applications in Earth observation
9.5. Applications in multi-sensor Earth observation
9.6. Conclusion
Chapter 10: Graph neural networks for multi-sensor Earth observation
10.1. Introduction
10.2. Graph in remote sensing image analysis
10.3. Basics of GNN
10.4. GNN in Earth observation
10.5. GNN in multi-sensor Earth observation
10.5.1. Multi-sensor domain adaptation
10.5.2. Multi-sensor classification with limited samples
10.5.3. Pansharpening
10.5.4. Multi-sensor big data analysis
10.5.5. Challenges
10.6. Conclusion
Chapter 11: Uncertainty quantification in deep neural networks for multi-sensor Earth observation
11.1. Introduction
11.2. Foundations of uncertainty quantification
11.2.1. Problem formulation
11.2.2. Sources of uncertainty
11.2.3. Types of uncertainty
11.3. Uncertainty quantification techniques
11.3.1. Single deterministic methods
11.3.2. Bayesian methods
11.3.3. Ensemble methods
11.3.4. Test-time augmentation methods
11.4. Uncertainty measures
11.5. Applications in multi-sensor Earth observation
11.5.1. Distributional uncertainty
11.5.2. Uncertainty-aware learning with label noise
11.5.3. Semantic segmentation
11.5.4. Optical-SAR fusion
11.5.5. Multi-sensor input importance estimation
11.6. Conclusion
Part IV: Multi-sensor deep learning applications
Chapter 12: Multi-sensor deep learning for change detection
12.1. Introduction
12.2. Problem formulation and data scenarios
12.3. Traditional CD methods
12.3.1. Algebra-based methods
12.3.2. Transformation-based methods
12.3.3. Classification-based methods
12.3.4. Object-based and texture-based methods
12.4. Deep learning in CD
12.4.1. Unsupervised methods
12.4.2. Supervised methods
12.4.3. Semi-supervised methods
12.5. Multi-sensor CD
12.5.1. Without deep learning
12.5.2. With deep learning
12.6. Datasets
12.7. A case study on multi-sensor confident pixel selection
12.7.1. Objective
12.7.2. Scope
12.7.3. Audience
12.7.4. Rationale
12.7.5. Expected results
12.7.6. Safety considerations
12.7.7. Method
12.7.8. Results
12.7.9. Learning outcomes
12.8. A case study on unsupervised multi-modal deep learning burned-area change detection
12.8.1. Objective
12.8.2. Datasets
12.8.3. Experimental settings
12.8.4. Experimental results
12.9. Conclusion
Chapter 13: Multi-sensor deep learning for glacier mapping
13.1. Introduction
Motivation
Structure
13.2. Glacier mapping with deep learning
Glacier mapping
Deep learning
13.3. Data modalities
Optical (multi-spectral) imagery
Synthetic aperture radar (SAR)
Digital elevation model
13.4. Literature overview
13.4.1. Glacier extent mapping
13.4.2. Calving front detection
13.5. Discussion
13.5.1. Recommendations
13.5.2. Deep learning for modelling glacier mass balance and their evolution
13.5.3. Outlook
13.6. Summary
13.7. Resources
Chapter 14: Deep learning in multi-sensor agriculture and crop management
14.1. Introduction
14.2. Data collection and management
14.2.1. Data types essential for multi-sensor agriculture
14.3. Traditional methods for precision agriculture
14.4. Deep learning in precision agriculture: concepts and applications
14.4.1. Deep learning with single-sensor data
14.4.2. Deep learning with multi-sensor data
14.5. Conclusion
Chapter 15: Miscellaneous applications of deep learning based multi-sensor Earth observation
15.1. Introduction
15.2. Urban monitoring
15.3. Mineral exploration
15.4. Marine applications
15.5. Forestry applications
15.6. Other environmental applications
15.7. Case study on tailings dam disaster detection
15.7.1. Objective
15.7.2. Scope
15.7.3. Audience
15.7.4. Rationale
15.7.5. Expected results
15.7.6. Method
15.7.7. Results
15.7.8. Learning outcomes
15.8. Case study on multi-sensor built area segmentation
15.8.1. Objective
15.8.2. Scope
15.8.3. Audience
15.8.4. Rationale
15.8.5. Expected results
15.8.6. Method
15.8.7. Results
15.8.8. Learning outcomes
15.9. Case study on cross-band wildfire impact detection
15.9.1. Objective
15.9.2. Scope
15.9.3. Audience
15.9.4. Rationale
15.9.5. Expected results
15.9.6. Method
15.9.7. Results
15.9.8. Learning outcomes
15.10. Conclusion
Chapter 16: Multi-sensor Earth observation: outlook
16.1. Introduction
16.2. Vision foundation models
16.3. Segment anything
16.4. Case study on built area segmentation from SAR image using segment anything
16.4.1. Objective
16.4.2. Scope
16.4.3. Audience
16.4.4. Rationale
16.4.5. Expected results
16.4.6. Workflow
16.4.7. Results
16.4.8. Learning outcomes
16.5. CLIP
16.6. Generative vision via diffusion models
16.7. Explainable AI
16.8. Computationally efficient Earth observation
16.9. Final words
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Deep Learning for Multi-Sensor Earth Observation

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